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A heuristic genetic algorithm for flowshop scheduling

机译：流动调度的启发式遗传算法

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Flowshop scheduling deals with determining the optimum sequence of jobs to be processed on several machines so as to satisfy some scheduling criterion. It is NP-complete. Heuristic algorithms use problem-specific information to yield good working solution. Genetic algorithms are stochastic, adaptive, general-purpose search heuristics based on concepts of natural evolution. We have developed a new heuristic genetic algorithm (NGA) which combines the good features of both the GA and heuristic search. The NGA is run on several problems and its performance is compared with that of the conventional genetic algorithm and the well-known NEH heuristic. The NGA is seen to perform better in almost all instances.
机译：Flowshop调度处理涉及在几台机器上处理要处理的最佳作业序列，以满足一些调度标准。它是np-cleante。启发式算法使用特定问题的信息来产生良好的工作解决方案。基于自然演化概念，遗传算法是随机，自适应，通用搜索启发式的启发式。我们开发了一种新的启发式遗传算法（NGA），它结合了GA和启发式搜索的良好特征。 NGA在几个问题上运行，并且其性能与传统遗传算法和众所周知的NEH启发式的性能进行了比较。在几乎所有情况下，NGA被视为更好。

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